Data Pipeline Development: Design, develop, and maintain robust, scalable, and high-performance data pipelines for efficient data extraction, transformation, and loading (ETL).
Data Architecture Design: Collaborate with data architects and stakeholders to design data infrastructure and architectures that meet the needs of analytics, reporting, and machine learning systems.
Database Management: Develop and optimize database schemas, queries, and stored procedures. Ensure efficient data storage, retrieval, and data integrity.
Big Data Technologies: Leverage big data technologies (e.g., Hadoop, Spark, Kafka) to handle large datasets and real-time streaming data.
Cloud Platforms: Build and deploy data engineering solutions on cloud platforms (e.g., AWS, Azure, Google Cloud), including using cloud-based storage and compute services like S3, Redshift, BigQuery, or Snowflake.
Data Quality & Governance: Implement data quality checks, monitoring, and validation processes to ensure data accuracy, consistency, and compliance with governance standards.
Automation & Monitoring: Automate data processing workflows, set up monitoring tools to detect issues, and improve system reliability and performance.
Collaboration: Work closely with cross-functional teams (data scientists, analysts, business stakeholders) to understand data requirements and ensure seamless data flow for analytics and reporting.
Mentoring: Provide technical guidance and mentorship to junior data engineers, helping them grow their skills and ensuring best practices are followed.
Documentation: Document data engineering processes, data models, pipeline workflows, and architecture to ensure knowledge transfer and system sustainability.